Multi-modal framework for multi-channel target speech separation

ABSTRACT

A method, computer program, and computer system for separating a target voice from among a plurality of speakers is provided. Video data associated with the plurality of speakers and audio data associated with each of the one or more speakers are received. Video feature data is extracted from the received video data. The target voice is identified from among the plurality of speakers based on the received audio data and the extracted video feature data.

BACKGROUND

This disclosure relates generally to field of computing, and moreparticularly to speech recognition.

Target speech separation extracts the speech of interest from anobserved speech mixture. With the entry into deep learning era, mostexisting supervised approaches are based on spectrogram masking, wherethe weight (mask) of the target speaker at each time-frequency (T-F) binof mixture spectrogram is estimated. As a result, the multiplicativeproduct between the mixture spectrogram and the predicted mask is servedas the target speech spectrogram.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forseparating a target voice from among a plurality of speakers. Accordingto one aspect, a method for separating a target voice from among aplurality of speakers is provided. The method may include receivingvideo data associated with the plurality of speakers and receiving audiodata associated with each of the one or more speakers. Video featuredata may be extracted from the received video data. The target voice maybe identified from among the plurality of speakers based on the receivedaudio data and the extracted video feature data.

According to another aspect, a computer system for separating a targetvoice from among a plurality of speakers is provided. The computersystem may include one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage devices, andprogram instructions stored on at least one of the one or more storagedevices for execution by at least one of the one or more processors viaat least one of the one or more memories, whereby the computer system iscapable of performing a method. The method may include receiving videodata associated with the plurality of speakers and receiving audio dataassociated with each of the one or more speakers. Video feature data maybe extracted from the received video data. The target voice may beidentified from among the plurality of speakers based on the receivedaudio data and the extracted video feature data.

According to yet another aspect, a computer readable medium forseparating a target voice from among a plurality of speakers isprovided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving video data associated with the plurality of speakersand receiving audio data associated with each of the one or morespeakers. Video feature data may be extracted from the received videodata. The target voice may be identified from among the plurality ofspeakers based on the received audio data and the extracted videofeature data.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a block diagram of a system for separating a target voice fromamong a plurality of speakers, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the operations carriedout by a program that separates a target voice from among a plurality ofspeakers, according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1 , according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5 , according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of computing, and moreparticularly to speech recognition. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, separate a target voice from among a plurality ofspeakers. Therefore, some embodiments have the capacity to improve thefield of computing by allowing for isolation of a single speaker voice'sfrom data containing the voices of multiple speakers based on extractingaudio and video features from the data.

As previously described, target speech separation extracts the speech ofinterest from an observed speech mixture. With the entry into deeplearning era, most existing supervised approaches are based onspectrogram masking, where the weight (mask) of the target speaker ateach time-frequency (T-F) bin of mixture spectrogram is estimated. As aresult, the multiplicative product between the mixture spectrogram andthe predicted mask is served as the target speech spectrogram. However,these approaches only use audio information, termed as audio-onlyapproaches, often suffering from intense interferences in complexacoustic environment, such as noise and reverberation. Since theacoustic target information can be blurry in the challenging acousticenvironment, other modalities can provide complementary and steadyinformation to increase the robustness. Therefore, it may beadvantageous to use a general multi-modal framework, aimed at extractingspeech of a desired speaker in a synchronized video and multi-channelaudio corrupted by noise and reverberation.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that extracts and integrates multi-modal separationcues from multi-channel speech mixture, target speaker's lip movements,and voice recordings to boost the performance. Additionally, in order toefficiently explore and exploit the high-level correlation betweenmodalities, a factorized attention based multi-modality fusion methodmay be used for target speech separation. Specifically, a multi-streamstructure may take the multi-channel mixture, mouth images cropped froma synchronized video, and an enrollment utterance of the target speakeras input and may extract the target speech with all other interferingsignals suppressed. By leveraging the microphone array-based signalprocessing techniques, the audio stream can fully utilize the spectraland spatial property of multi-channel speech signal to obtain a morecomplete and robust target-related acoustic representation. The videostream may capture the spatio-temporal dynamics of mouth movements andproduces lip embeddings. The speaker embedding stream may map a cleanreference audio of the target speaker to a feature vector which containsthe speaker-specific information. A factorized attention-basedaggregation method may be used for fusing the high-level semanticinformation of multi-modalities at embedding level.

Referring now to FIG. 1 , a functional block diagram of a networkedcomputer environment illustrating a target voice separation system 100(hereinafter “system”) for separating a target voice from among aplurality of speakers. It should be appreciated that FIG. 1 providesonly an illustration of one implementation and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 6 and 7 . The server computer 114 may also be locatedin a cloud computing deployment model, such as a private cloud,community cloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for separating a target voicefrom among a plurality of speakers is enabled to run a Target VoiceSeparation Program 116 (hereinafter “program”) that may interact with adatabase 112. The Target Voice Separation Program method is explained inmore detail below with respect to FIG. 3 . In one embodiment, thecomputer 102 may operate as an input device including a user interfacewhile the program 116 may run primarily on server computer 114. In analternative embodiment, the program 116 may run primarily on one or morecomputers 102 while the server computer 114 may be used for processingand storage of data used by the program 116. It should be noted that theprogram 116 may be a standalone program or may be integrated into alarger target voice separation program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 maybe implemented within a single device, or a single device shown in FIG.1 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2 , a block diagram 200 of a multi-modal targetspeech separation system is depicted. The multi-modal target speechseparation system may include, among other things, a directional featureextractor 202, a lip embedding network 204, a speaker embedding network206, and a multi-modal separation network 208. The multi-modal speechseparation system may receive video data 210 and audio data 212 asinputs. The multi-modal separation network 208 may output target speechdata 214.

The directional feature extractor 202 may take a noisy, multi-channelspeech mixture and the target speaker's direction as input, in order toextract acoustic embeddings. A short-time Fourier transform (STFT)convolution 1D layer may be used to map the multi-channel mixturewaveforms to complex spectrograms. Based on the complex spectrograms,the single-channel spectral feature (i.e., logarithm power spectra, LPS)and multi-channel spatial feature (i.e., interaural phase differences,IPD) may be extracted. Apart from the target speaker independentspectral and spatial features, a directional feature may be extractedaccording to the spatial direction of target speaker. All of thefeatures may be concatenated and fed into audio blocks, which mayinclude stacked dilated convolutional layers with exponentially growingdilation factors. The output of the audio blocks may include one or moreacoustic embeddings. On the system output side, an iSTFT convolution 1Dlayer may be used to convert the estimated target speaker complexspectrogram back to the waveform.

The lip embedding network 204 may extract frame-level lip embeddingsfrom the video data 210. The lip embedding network 204 may receivecropped mouth images of the target speaker as input. The lip embeddingnetwork 204 may include a temporal-spatio convolution layer and a18-layer ResNet. The supervision information may be formed from an audiodomain, which may use the video data 210 to discover one or morecross-domain correlations between the target speech and lip movements.The lip embedding network 204 may utilize one or more video blocks thatmay each contain several dilated temporal convolutional layers withresidual connections. The output of the video blocks may include one ormore lip embeddings. Since the resolution of the video and audio streamsmay be different, the lip embeddings may be upsampled to synchronizewith the audio stream by nearest neighbor interpolation.

The speaker embedding network 206 may process the enrollment audio of atarget speaker, such as the audio data 212, and may generate anutterance-level speaker embedding. Speaker embedding may be a biassignal that may inform the multi-modal separation network 208 to performand enhance target speaker separation. A pre-trained speaker model maybe introduced and utilized for producing speaker embeddings tocharacterize the target speaker. The speaker model may be pre-trained onthe task of speaker verification, using one or more convolution layersfollowed by a fully connected layer. The input to the speaker model mayan enrollment utterance of the target speaker, such as saying one's namewhen entering a teleconference. The speaker embedding network 206 mayoutput the utterance-level speaker embedding.

The multi-modal separation network 208 may combine the acoustic, lip,and speaker embeddings and feed into one or more fusion blocks, whichmay output a T-F mask for the target speaker. The T-F mask may be usedto estimate the target speech waveform. As described above, three kindsof target information may be derived from a set of media sources,including acoustic embeddings from multi-channel speech, lip embeddingsfrom the video and speaker embedding from the target speaker'senrollment. In order to learn effective target speech extraction frommulti-modal information, a factorized layer may be used for fastadaptation to the acoustic context. In speech recognition, a factor maybe a set of speakers or a specific acoustic environment. The factorizedlayer may use a different set of parameters to process each acousticclass, which may depend on external factors that represent the acousticconditions. According to one or more embodiments, the acousticembeddings may be factorized into a set of acoustic subspaces (e.g.,phone subspaces, speaker subspaces) and may utilize information fromother modalities to aggregate them with selective attention. The othermodalities can also provide information related to the acousticcondition, such as voice activity interpreted from the opening andclosing of mouth, and target speaker voice characteristics contained inthe speaker embedding. Specifically, the acoustic embeddings A may befactorized into different acoustic subspaces with parallel lineartransformations W_(a) ¹, W_(a) ², . . . , W_(a) ^(H), where H may be thenumber of subspaces and the acoustic representation in h-th subspace atthe t-th time step may be denoted as a_(t) ^(h)=A_(t)W_(a) ^(h)∈□^(1×P),where P is the output dimension of the linear transform. The lipembeddings V may also be mapped from the D-dimensional space to aH-dimensional space, where each dimension h may contain bias informationthat may correspond to the h-th acoustic subspace. The mapped lipembeddings may be passed to a softmax layer and may produce theestimated posterior for each subspace at each time step, calculated asv=softmax(VW_(v))=[v¹, v², . . . , v^(H)]∈□^(T×H). The fusedaudio-visual embedding (AVE) may be obtained by summing the weightedcontribution of different acoustic subspaces:

${AVE}_{t} = {\sigma\left( {\sum\limits_{h = 1}^{H}{v_{t}^{h}a_{t}^{h}}} \right)}$where σ may be the sigmoid activation function. The acoustic and speakerembeddings may be similarly combined and may be calculated as:ASE_(t)=σ(Σ_(h=1) ^(H)(softmax(S _(t) W _(s)))^(h) a _(t) ^(h)),where W_(s) may be the weight matrix that may convert the speakerembedding S from speaker space to acoustic subspaces.

Referring now to FIG. 3 , an operational flowchart 300 illustrating theoperations carried out by a program that separates a target voice fromamong a plurality of speakers is depicted. FIG. 3 may be described withthe aid of FIGS. 1 and 2 . As previously described, the Target VoiceSeparation Program 116 (FIG. 1 ) may quickly and effectively utilizeaudio and video feature data to isolate the voice of a target speakerfrom among a plurality of other speakers' voices.

At 302, video data associated with the plurality of speakers isreceived. It may be appreciated that any number of speakers may becaptured by any number of cameras. In operation, the Target VoiceSeparation Program 116 (FIG. 1 ) on the server computer 114 may receivethe video data 210 (FIG. 2 ) from one or more computers 102 (FIG. 1 )over the communication network 110 (FIG. 1 ).

At 304, audio data associated with each of the one or more speakers isreceived. The audio data may include an enrollment utterance, such as aspeaker saying their name when entering a teleconference. In operation,the Target Voice Separation Program 116 (FIG. 1 ) on the server computer114 may receive the audio data 212 (FIG. 2 ) from one or more computers102 (FIG. 1 ) over the communication network 110 (FIG. 1 ). The audiodata 212 may be passed to the speaker embedding network 206 (FIG. 2 ).

At 306, video feature data is extracted from the received video data.The video feature data may include a direction associated with a cameracapturing the one or more speakers and lip movement image dataassociated with each of the one or more speakers. In operation, thedirectional feature extractor 202 (FIG. 2 ) may extract direction datafrom the video data 210 (FIG. 2 ). The lip embedding network 204 (FIG. 2) may further extract lip movement data from the video 210.

At 308, the target voice is identified from among the plurality ofspeakers based on the received audio data and the extracted videofeature data. In operation, the multi-modal separation network 208 (FIG.2 ) may receive the extracted direction data from the directionalfeature extractor 202 (FIG. 2 ), the extracted lip movement data fromthe lip embedding network 204 (FIG. 2 ), and features extracted from theaudio data 212 (FIG. 2 ) from the speaker embedding network 206 (FIG. 2). The multi-modal separation network 208 may isolate a target speaker'svoice and may output the target speech data 214 (FIG. 2 ).

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1 ) and server computer 114 (FIG. 1 ) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4 . Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1) and the Target Voice Separation Program 116 (FIG. 1 ) on servercomputer 114 (FIG. 1 ) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMS 822 (which typically include cache memory). In the embodimentillustrated in FIG. 4 , each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1 ) and the Target Voice Separation Program 116 (FIG.1 ) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1 ) and theTarget Voice Separation Program 116 (FIG. 1 ) on the server computer 114(FIG. 1 ) can be downloaded to the computer 102 (FIG. 1 ) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Target VoiceSeparation Program 116 on the server computer 114 are loaded into therespective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5 , illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6 , a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Target Voice Separation 96. Target VoiceSeparation 96 may separate a target voice from among a plurality ofspeakers.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of separating a target voice from amonga plurality of speakers, the method comprising: receiving video dataassociated with the plurality of speakers; receiving audio dataassociated with each of the one or more speakers, the audio datacomprising first audio data, synchronized to the video data, and secondaudio data comprising an enrollment utterance of at least one of thespeakers pre-recorded prior to recording the video data; extractingvideo feature data from the received video data; identifying the targetvoice from among the plurality of speakers based on a mask generated byusing inputs comprising at least the first audio data, the second audiodata, and the extracted video feature data.
 2. The method of claim 1,wherein the extracted video feature data comprises direction datacorresponding to the one or more users.
 3. The method of claim 1,wherein the extracted video feature data comprises lip movement datacorresponding to each of the one or more speakers.
 4. The method ofclaim 3, wherein the lip movement data comprises cropped imagescorresponding to the mouths of each of the one or more speakers.
 5. Themethod of claim 1, wherein the audio data comprises an enrollmentutterances, including the enrollment utterance, associated with each ofthe one or more speakers.
 6. The method of claim 1, wherein identifyingthe target voice comprises generating the mask as a time-frequency maskfor the target speaker.
 7. The method of claim 1, wherein the videofeature data is extracted using a convolutional neural network.
 8. Acomputer system for separating a target voice from among a plurality ofspeakers, the computer system comprising: one or more computer-readablenon-transitory storage media configured to store computer program code;and one or more computer processors configured to access said computerprogram code and operate as instructed by said computer program code,said computer program code including: first receiving code configured tocause the one or more computer processors to receive video dataassociated with the plurality of speakers; second receiving codeconfigured to cause the one or more computer processors to receive audiodata associated with each of the one or more speakers, the audio datacomprising first audio data, synchronized to the video data, and secondaudio data comprising an enrollment utterance of at least one of thespeakers pre-recorded prior to recording the video data; extracting codeconfigured to cause the one or more computer processors to extract videofeature data from the received video data; identifying code configuredto cause the one or more computer processors to identify the targetvoice from among the plurality of speakers based on a mask generated byusing inputs comprising at least the first audio data, the second audiodata, and the extracted video feature data.
 9. The computer system ofclaim 8, wherein the extracted video feature data comprises directiondata corresponding to the one or more users.
 10. The computer system ofclaim 8, wherein the extracted video feature data comprises lip movementdata corresponding to each of the one or more speakers.
 11. The computersystem of claim 10, wherein the lip movement data comprises croppedimages corresponding to the mouths of each of the one or more speakers.12. The computer system of claim 8, wherein the audio data comprisesenrollment utterances, including the enrollment utterance, associatedwith each of the one or more speakers.
 13. The computer system of claim8, wherein identifying the target voice comprises generating the mask asa time-frequency mask for the target speaker.
 14. The computer system ofclaim 8, wherein the video feature data is extracted using aconvolutional neural network.
 15. A non-transitory computer readablemedium having stored thereon a computer program for separating a targetvoice from among a plurality of speakers, the computer programconfigured to cause one or more computer processors to: receive videodata associated with the plurality of speakers; receive audio dataassociated with each of the one or more speakers, the audio datacomprising first audio data, synchronized to the video data, and secondaudio data comprising an enrollment utterance of at least one of thespeakers pre-recorded prior to recording the video data; extract videofeature data from the received video data; identify the target voicefrom among the plurality of speakers based on a mask generated by usinginputs comprising at least the first audio data, the second audio data,and the extracted video feature data.
 16. The computer readable mediumof claim 15, wherein the extracted video feature data comprisesdirection data corresponding to the one or more users.
 17. The computerreadable medium of claim 15, wherein the extracted video feature datacomprises lip movement data corresponding to each of the one or morespeakers.
 18. The computer readable medium of claim 17, wherein the lipmovement data comprises cropped images corresponding to the mouths ofeach of the one or more speakers.
 19. The computer readable medium ofclaim 15, wherein the audio data comprises enrollment utterances,including the enrollment utterance, associated with each of the one ormore speakers.
 20. The computer readable medium of claim 15, whereinidentifying the target voice comprises generating the mask as atime-frequency mask for the target speaker.